Overlay Relationships
This week’s assignment will encompass the following concepts covered in Class 7 lecture & lab:
This week’s readings will include 1 section from the Essentials of Geographic Information Systems textbook; further, the supplemental technical readings cover best approaches to choosing and using map projections.
The class 7 quiz will cover only content from the Essentials of Geographic Information Systems textbook as follows:
Chapter 5 - Geospatial Data Management
Chapter 8 - Spatial Analysis of Vector Data
Context Reading (following material will not be quizzed, but helpful to understand assignment context):
International Committee of the Red Cross - Explosive Remnants of War
For further discussion and terminology related to Toxic Remnants of War, see website.
Assignment Preamble:
In this assignment, you will create 1 map deliverable that will thematically summarize a series of overlay relationships of ERW (Explosive Remnants of War) and resources (populations, infrastructure, ect.) requiring risk assessment and protection across Kosovo within district level administrative boundaries.
As shown in the following chart, there are a series of 8 progressive steps to achieve the final thematic map deliverable:
Assignment Step Chart
The assignment workflow will feature the following components:
.gdb structure:Assignment .gdb structure
Note: not all layers in the
.gdbwill be utilized for this assignment.
Set C7_asgmt_data folder as working file path and folder, and create a new folder inside the C7_asgmt_data folder , name Exports - this folder can hold geoprocessing outputs developed during this assignment. If you do not assign a folder, just be aware of the location output of your geoprocessing as you will need to access those processed outputs throughout the assignment.
Connect to data source:
Connect to .gdb
Import Point and Polygon Features
map view/canvas:Data View of Assignment Data
Projections Verification:
Prior to beginning any geoprocessing with QGIS, it is critical to check that the map projection is consistent across all analysis layers. Your layers should all contain the following CRS parameters. While QGIS sees these specific parameters as ‘user defined’, it is a known projection and coordinate system known as the ETRS 1989 Kosovo Grid. Note that the unit measurement is meters, so all geoprocessing processes will utilize meter as default map unit.
QGIS user defined parameters:
+proj=tmerc +lat_0=0 +lon_0=21 +k=0.9999 +x_0=7500000 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
Kosovo Grid Project Definition
Geoprocess Step #1 - Proximity Buffers:
Create BUFFERS
UXOLandmineSites Buffer = 100 meters
ClusterBombs Buffer = 500 meters
Save all resulting buffer features in the assignment folder outside the .gdb. Name the folder exports.
UXOLandmineSites Buffer parameters:
Land Mine Buffers
Note: all buffers created in this assignment can abide by a segment count of 30. Increasing the segment count creates a more precise boundary polyline.
Land Mine Buffer Results
Cluster Bomb Buffers
Cluster Bomb Buffer Results
Geoprocess Step #2 - Union:
UNION ClusterBombSites Buffer + UXOLandmineSites Buffer:
Union Parameters
Note: The UNION will result in more than 1 record, which at this stage is acceptable. Before proceeding to the final analysis, it must become just one polygon prior to CLIPPING which will be done in succeeding steps for
Dissolvein Geoprocess Step #3:
Union Results
Geoprocess Step #3 - DISSOLVE:
The UNION can be fully dissovled into one polygon feature through several avenues within QGIS. One foolproof appproach is to make a new field entitled dissolve_C (dissolve calculation) and populate it with the field calculator with a simple number, in this case 1:
Dissolve via an Attribute Column
field calculator and populate with integer 1:Integer Assignment of 1
dissolve_C value 1:Integer Assignment of 1 - Check Result
GDAL within GRASS tools from the main menu Toolbox, choose Dissolve Polygons and populate the parameters for Dissolve as follows:Dissolve Polygons -Result (GDAL Tool)
Dissolve should result in one polygon feature, validate this result before proceeding:Dissolve Polygons -Result (GDAL Tool)
Note: if you are not able to produce a result from the
dissolveprocess above, try producing a temporary file as an alternative:
Run Tool w/ Temporary Output
Geoprocess Step #4 - CLIPPING:
At this juncture, the ERW area has been determined and dissolved into one feature, but there is no way to determine where ERW starts and stops within a particular district(s). To accomplish this task Clip will be utilized.
Populate the parameters for Clip process as follows:
Clip Tool
Clip results in breaks in the previous dissolve at district edges as seen below. Also verify that a total of 235 records results in the Clipped layer. Note: the AREA column is from the district layer, transposed into the Clipped attribute table. In succeeding steps, we will use this total sq. area per district to calculate a final % contaminated per district.Visually Inspect Clipped Features
Geoprocess Step #5 - Add Geometry Column:
Utilizing the Add Geometry Column function under Vector Tools, create this column. Note: in the following image Clipped has been renamed contaminated_districts to distinquish its content over its geoprocessing function:
Add Geometry Column
area_1 as well as perimeter. These attributes are the sq. area and perimeter length of the Clipped ERW per district.Add Geometry Column - Result
Tabular Calculations Step #6 - Derive % of contamination per district:
As a result of Step #5 above, both AREA for districts as well as area_1 for contaminated sq. area (meters) now exists in the attribute table for contaminated_districts, the result of the Clip geoprocessing. The following steps will create a normalized column for % of each district that is contaminated. This is needed to know the relative levels of contamination across all districts in Kosovo.
Use the following expression for % contaminated:
"area_1" / "AREA" * 100To start, use Add field to create a new column that will house % contaminated. Name the field/column PCTCNTM:
Create Column for % Contaminated
Resulting Empty Column
Field calculator to derive % contaminated per districts:% Calculation in Field Calculator
PCTCNTM for each district record:% Calculated in New Column
Tabular Calculations Step #7 - Determine categorical classifcation ‘level of contamination’:
While normalized % contamination now exists in the table, a category of ‘level of contamination’ is a more effective approach for priority mapping. In this step both thresholds and a range will be used to develop levels based on the following criteria:
| Level Description | Definition |
|---|---|
| None | 0 percent |
| Low | <1.3 percent (up to the median value of contamination) |
| Medium | 1.3-6.5 (between one and five times the median value of contamination) |
| High | >6.5 percent (more than five times the median value of contamination) |
LVLCONTM via Add field, text type with an length of 10. This will be the column for Level Descriptions:Level Column
Select by expression utilizing "PCTCNTM" <1.3 as the first expression for ‘low’Note: in the image below it is inadvertently >=1.3 which is not ‘low’; use <1.3 instead:
Low Classification
Field calculator, assign ‘Low’ value to LVLCONTM, using update exisitng field, not create a new field option:Low Classification Assignment
Deselect Features from All Layers following each level assignment. This will ensure that selections are not being added incorrectly to proceeding selections; that is, each selection for the 3 values low, medium and high are separate:Imgur
Following the "PCTCNTM" <1.3 expression and level assignnent for ‘low’, continue to enact ‘medium’ and ‘high’ based on the following expressions:
Medium: "PCTCNTM" >=1.3 and "PCTCNTM" <=6.5
High: "PCTCNTM" >6.5
Tabular Calculations Step #8 - Join contamination ranks table to original district feature in preparation for final thematic mapping:
After finishing Step #7, export the feature to exports folder as contamination_table. This features houses the 235 districts that contain various levels of contamination. This tabular data must be joined to the original district feature. The result will be 235 districts with full attributes of both features/tables, and remaining districts with NULL records for contamination, meaning they do not have any contamination and can thus be mapped ‘none’ or ‘no contamination’.
Export Feature with Calculations Completed in Attribute Table
Navigate to the original districts feature in Layer panel, properties > join. Utilize the unique OBJECTID field that is common between contaminated_table and districts and create the join:
Join on OBJECTID
district_joined - this will finalize the join, making it permanent to the feature. If this is not done, the join is not permanent to the feature:Export Successful Join
Thematic Mapping Step #9 - Thematically map ‘Level of ERW Contamination’:
Utilize the LVLCONTM column as input into categorical symbols for thematic mapping. Design your classes so that those districts with high appear dense, concentrated relative to medium and low classes. Make sure to visually segment the districts that have no contamination separate from the classes that do indeed have some level of contamination as its especially important to distinquish none from low.
Thematic Design for Contamination Levels per District
item properties for the legend to fine tune and revise:Classification based on Categorical Values using a Sequence Approach
Assignment #7 Deliverable:
8.5x11” thematic map PDF titled ‘Levels of ERW Contamination - Districts, Kosovo War’ Like the map examples below, you will map thematically using district boundaries for contamination. No need for scale bar or north arrow - just the map results, thematic legend and data source.
Data Source - Mine Action Coordination Centre (MACC)
Refer to this week’s map examples also for guidance on the appearance of the map.